Dechao Chen

Electrochemical nitrate reduction reaction (NO ₃ ⁻ RR) provides a sustainable approach for both NO ₃ ⁻ purification and NH ₃ production. Ru-based catalysts hold great promise for NO ₃ ⁻ RR, but are limited by competing hydrogen evolution reaction, insufficient electrochemical stability, and the sluggish thermodynamics and kinetics of the initial *NO ₃ → *NO ₂ reduction step. Here, we develop an inside-out strategy by integrating ultrasmall Ru nanoparticles on the outer surface of carbon nanotubes and confined amorphous CuO _x nanowires inside (CuO _x @CNT/Ru) to enhance NH ₃ synthesis from NO ₃ ⁻ RR. This catalyst achieves a leading NH ₃ yield rate of 146.37±3.4 mg h ⁻¹ mg _cat ⁻¹ at −0.7 V vs. reversible hydrogen electrode (vs. RHE), a Faradaic efficiency of 99.1 ± 0.9% at 0 V vs. RHE, and the highest energy efficiency of 43.5 ± 0.9% at 0 V vs. RHE. Moreover, as a Zn−NO ₃ ⁻ battery cathode, CuO _x @CNT/Ru delivers a maximum power density of 22.6 mW cm ⁻² along with high NH ₃ production efficiency. In situ spectroscopic analysis and theoretical calculations reveal that Ru species serve as the main active centers, while high-valence CuO _x not only stabilizes and activates Ru sites but also facilitates the conversion of *NO ₃ to *NO ₂ and promotes active hydrogen generation from water dissociation, thereby accelerating the hydrogenation kinetics of nitrogen-containing intermediates and reducing the energy barrier of the rate-determining step of *NO to *NOH, ultimately boosting NH ₃ synthesis. This work provides an efficient strategy for modulating active site interactions to promote sustainable nitrate reduction processes.

Metallocene catalysts, distinguished by their well-defined active centers and tunable coordination geometries, are pivotal in the homopolymerization of propylene to produce polypropylene with tailored properties. However, the rational design of such catalysts remains challenging due to the complex coupling between ligand structures and polymerization conditions. Conventional trial-and-error approaches are inefficient, while existing machine learning (ML) models often overlook critical ligand descriptors, limiting their generalization for industrial use. To address this, we developed a hybrid ML framework that integrates both reaction parameters and catalyst structural features. A dual-path neural network processes numerical and categorical inputs separately to avoid feature semantic distortion, enabling accurate predictions of catalyst activity (R² = 0.9201) and number-average molecular weight (R² = 0.9133). For the narrow molecular weight distribution typical of metallocene-derived polypropylene─a characteristic leading to compact, locally correlated data─a k-nearest neighbor regression model achieved superior performance (R² = 0.9766) by effectively capturing local sample relationships. Both models outperformed eight other benchmark ML algorithms across all metrics. This work provides a robust, interpretable computational strategy for linking catalyst chemistry to polymer properties, offering a practical tool for the targeted design and scalable application of high-performance polypropylene materials.